Some studies have reported promising results on the use of Support Vector Machines (SVMs) for predicting fault-prone software components. Nevertheless, the performance of the method heavily depends on the setting of some parameters. To address this issue, we investigated the use of a Genetic Algorithm (GA) to search for a suitable configuration of SVMs to be used for inter-release fault prediction. In particular, we report on an assessment of the method on five software systems. As benchmarks we exploited SVMs with random and Grid-search configuration strategies and several other machine learning techniques. The results show that the combined use of GA and SVMs is effective for inter-release fault prediction.

A Further Analysis on the Use of Genetic Algorithm to Configure Support Vector Machines for Inter-Release Fault Prediction

SARRO, FEDERICA;FERRUCCI, Filomena;GRAVINO, Carmine
2012

Abstract

Some studies have reported promising results on the use of Support Vector Machines (SVMs) for predicting fault-prone software components. Nevertheless, the performance of the method heavily depends on the setting of some parameters. To address this issue, we investigated the use of a Genetic Algorithm (GA) to search for a suitable configuration of SVMs to be used for inter-release fault prediction. In particular, we report on an assessment of the method on five software systems. As benchmarks we exploited SVMs with random and Grid-search configuration strategies and several other machine learning techniques. The results show that the combined use of GA and SVMs is effective for inter-release fault prediction.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/3864386
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